harshankumarhrk/depression_sih

Depression Prediction

PROBLEM STATEMENT:

According to WHO ( World Health Organisation ), there are 8 billion people in the World out of which 130 million people visit the ER (Emergency Room) every year for any illness, sickness and other issues. It takes a Physician, a family doctor, a specialist and a chief doctor to diagnose a disease and make a critical decision about the treatment process. Therefore, it takes an average of at least a day or two to diagnose a deadly disease within which the disease could have spread further and made even more damage. Automating reading DTI MRI, MRI, CT scans and EMG scans of mood- affected and non-affected individuals Quantify the dominant fiber orientation Read Structural & Neural connections of the organs High-accuracy Computer Aided Diagnosis

PROPOSED SOLUTION:

The aim of the research is to automate and compute the detection of possible vulnerability to any disease before it’s onset. It is important for a Doctor to spend enough time on a patient’s diagnosis as it decides the course of treatment. But reading the scans manually could lead to errors in diagnosis and exhaust the working time of a doctor too. This situation can be avoided using a model that was created by Machine Learning and Data Analysis. Algorithms like SVM,CNN,LSTM etc can be used accordingly. This model can comprehend, analyze and automatically produce the health report of a patient in seconds thus saving time and lives at the same time. A questionnaire with symptom based question is trained with ML to diagnose diseases using the symptom by asking the patients directly.

Predict Depression using DTI-MRI Images using Machine Learning

Run the website with a single command

python routes.py

Run the depression chatbot

python chatbot.py

Dataset Links:

Healthy

https://drive.google.com/drive/folders/18z0fNBP0cEeB0_kouqUi23kUzRuWhsRS

Depressed

https://drive.google.com/drive/folders/1yXy_0t4UIpdE53nAYMap0Y3-kNdWwzG0